llama.cpp
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Published
20 hours ago •
ggml-org
ggml-org
Bug: BigLlama-3.1-681B-Instruct requires llama_model_max_nodes to return a higher value
What happened?
This issue is caused by a reappearance of issue https://github.com/ggerganov/llama.cpp/issues/8615 (PR https://github.com/ggerganov/llama.cpp/pull/8622). I recommend reading them for more information about this problem.
For Meta-Llama-3.1-405B-Instruct it turned out that the default llama_model_max_nodes of 8192 is still enough. For its self-merge available under https://huggingface.co/mlabonne/BigLlama-3.1-681B-Instruct (GGUFs available under https://huggingface.co/mradermacher/BigLlama-3.1-681B-Instruct-GGUF/tree/main) it is unfortunately not.
For this issue to be fixed the commented out logic under https://github.com/ggerganov/llama.cpp/blob/3071c0a5f218f107dabd13b73f6090af683ef5ec/src/llama.cpp#L3571-L3573 needs to be reenabled and set to something like model.hparams.n_layer > 200 for this model to work. Maybe a good approach would be having 0-200 return 8192, >200 return 16384 and >400 return 32768. To play around with this model I made the llama_model_max_nodes function always return 16384 which fixed the issue.
Name and Version
version: 3557 (3071c0a5) built with cc (Debian 12.2.0-14) 12.2.0 for x86_64-linux-gnu
What operating system are you seeing the problem on?
Linux
Relevant log output
./llama-cli -m /bpool/BigLlama-3.1-681B-Instruct.Q4_K_S.gguf -p "I believe the meaning of life is" -c 128 -n 64 -ngl 0
main: build = 3557 (3071c0a5)
main: built with cc (Debian 12.2.0-14) 12.2.0 for x86_64-linux-gnu
main: seed = 1723208630
llama_model_loader: loaded meta data with 32 key-value pairs and 1894 tensors from /bpool/BigLlama-3.1-681B-Instruct.Q4_K_S.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 405B Instruct
llama_model_loader: - kv 3: general.organization str = Meta Llama
llama_model_loader: - kv 4: general.finetune str = Instruct
llama_model_loader: - kv 5: general.basename str = Meta-Llama-3.1
llama_model_loader: - kv 6: general.size_label str = 405B
llama_model_loader: - kv 7: general.base_model.count u32 = 1
llama_model_loader: - kv 8: general.base_model.0.name str = Meta Llama 3.1 405B Instruct
llama_model_loader: - kv 9: general.base_model.0.organization str = Meta Llama
llama_model_loader: - kv 10: general.base_model.0.repo_url str = https://huggingface.co/meta-llama/Met...
llama_model_loader: - kv 11: general.tags arr[str,2] = ["mergekit", "merge"]
llama_model_loader: - kv 12: llama.block_count u32 = 210
llama_model_loader: - kv 13: llama.context_length u32 = 131072
llama_model_loader: - kv 14: llama.embedding_length u32 = 16384
llama_model_loader: - kv 15: llama.feed_forward_length u32 = 53248
llama_model_loader: - kv 16: llama.attention.head_count u32 = 128
llama_model_loader: - kv 17: llama.attention.head_count_kv u32 = 16
llama_model_loader: - kv 18: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 19: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 20: general.file_type u32 = 14
llama_model_loader: - kv 21: llama.vocab_size u32 = 128256
llama_model_loader: - kv 22: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 24: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", ...
llama_model_loader: - kv 28: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 29: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 30: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 31: general.quantization_version u32 = 2
llama_model_loader: - type f32: 422 tensors
llama_model_loader: - type q4_K: 1441 tensors
llama_model_loader: - type q5_K: 30 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 16384
llm_load_print_meta: n_layer = 210
llm_load_print_meta: n_head = 128
llm_load_print_meta: n_head_kv = 16
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 2048
llm_load_print_meta: n_embd_v_gqa = 2048
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 53248
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = ?B
llm_load_print_meta: model ftype = Q4_K - Small
llm_load_print_meta: model params = 680.67 B
llm_load_print_meta: model size = 359.76 GiB (4.54 BPW)
llm_load_print_meta: general.name = Meta Llama 3.1 405B Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.89 MiB
llm_load_tensors: CPU buffer size = 368397.47 MiB
....................................................................................................
llama_new_context_with_model: n_ctx = 128
llama_new_context_with_model: n_batch = 128
llama_new_context_with_model: n_ubatch = 128
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 210.00 MiB
llama_new_context_with_model: KV self size = 210.00 MiB, K (f16): 105.00 MiB, V (f16): 105.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.49 MiB
ggml/src/ggml-backend.c:1936: GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs) failed
./llama-cli(+0x5e208)[0x5b4ba469d208]
./llama-cli(+0x60655)[0x5b4ba469f655]
./llama-cli(+0xad7c6)[0x5b4ba46ec7c6]
./llama-cli(+0x10a400)[0x5b4ba4749400]
./llama-cli(+0x1d160c)[0x5b4ba481060c]
./llama-cli(+0x3ec0e)[0x5b4ba467dc0e]
/lib/x86_64-linux-gnu/libc.so.6(+0x2724a)[0x7b12ad92f24a]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0x85)[0x7b12ad92f305]
./llama-cli(+0x45521)[0x5b4ba4684521]
